The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity

Author:

Dvorak Adam V.12ORCID,Kumar Dushyant3ORCID,Zhang Jing4ORCID,Gilbert Guillaume5ORCID,Balaji Sharada12,Wiley Neale12,Laule Cornelia1267,Moore G.R. Wayne27,MacKay Alex L.167ORCID,Kolind Shannon H.1268

Affiliation:

1. Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.

2. International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada.

3. Radiology, University of Pennsylvania, Philadelphia, PA, USA.

4. Global MR Applications & Workflow, GE HealthCare Canada, Mississauga, ON, Canada.

5. MR Clinical Science, Philips Canada, Mississauga, ON, Canada.

6. Radiology, University of British Columbia, Vancouver, BC, Canada.

7. Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.

8. Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada.

Abstract

Quantitative magnetic resonance imaging (MRI) techniques are powerful tools for the study of human tissue, but, in practice, their utility has been limited by lengthy acquisition times. Here, we introduce the Constrained, Adaptive, Low-dimensional, Intrinsically Precise Reconstruction (CALIPR) framework in the context of myelin water imaging (MWI); a quantitative MRI technique generally regarded as the most rigorous approach for noninvasive, in vivo measurement of myelin content. The CALIPR framework exploits data redundancy to recover high-quality images from a small fraction of an imaging dataset, which allowed MWI to be acquired with a previously unattainable sequence (fully sampled acquisition 2 hours:57 min:20 s) in 7 min:26 s (4.2% of the dataset, acceleration factor 23.9). CALIPR quantitative metrics had excellent precision (myelin water fraction mean coefficient of variation 3.2% for the brain and 3.0% for the spinal cord) and markedly increased sensitivity to demyelinating disease pathology compared to a current, widely used technique. The CALIPR framework facilitates drastically improved MWI and could be similarly transformative for other quantitative MRI applications.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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